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Automated image processing pipeline for adaptive optics scanning light ophthalmoscopy.

Alexander E SalmonRobert F CooperMin ChenBrian HigginsJenna A CavaNickolas ChenHannah M FollettMina GaffneyHeather HeitkotterElizabeth HeffernanTaly Gilat SchmidtJoseph Carroll
Published in: Biomedical optics express (2021)
To mitigate the substantial post-processing burden associated with adaptive optics scanning light ophthalmoscopy (AOSLO), we have developed an open-source, automated AOSLO image processing pipeline with both "live" and "full" modes. The live mode provides feedback during acquisition, while the full mode is intended to automatically integrate the copious disparate modules currently used in generating analyzable montages. The mean (±SD) lag between initiation and montage placement for the live pipeline was 54.6 ± 32.7s. The full pipeline reduced overall human operator time by 54.9 ± 28.4%, with no significant difference in resultant cone density metrics. The reduced overhead decreases both the technical burden and operating cost of AOSLO imaging, increasing overall clinical accessibility.
Keyphrases
  • deep learning
  • high resolution
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